{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/04242.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkyLVN8Ee2_k",
        "colab_type": "text"
      },
      "source": [
        "## 3.5 图像分类数据集 ( Fashion-MNIST )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9ntg3Ex2_qQ",
        "colab_type": "text"
      },
      "source": [
        "### 3.5.1 获取数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBckG7A65-Yj",
        "colab_type": "code",
        "outputId": "5fb0ab36-ddfc-44c5-99c3-3440d4cea266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "!git clone https://github.com/ShusenTang/Dive-into-DL-PyTorch.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Dive-into-DL-PyTorch'...\n",
            "remote: Enumerating objects: 84, done.\u001b[K\n",
            "remote: Counting objects:   1% (1/84)\u001b[K\rremote: Counting objects:   2% (2/84)\u001b[K\rremote: Counting objects:   3% (3/84)\u001b[K\rremote: Counting objects:   4% (4/84)\u001b[K\rremote: Counting objects:   5% (5/84)\u001b[K\rremote: Counting objects:   7% (6/84)\u001b[K\rremote: Counting objects:   8% (7/84)\u001b[K\rremote: Counting objects:   9% (8/84)\u001b[K\rremote: Counting objects:  10% (9/84)\u001b[K\rremote: Counting objects:  11% (10/84)\u001b[K\rremote: Counting objects:  13% (11/84)\u001b[K\rremote: Counting objects:  14% (12/84)\u001b[K\rremote: Counting objects:  15% (13/84)\u001b[K\rremote: Counting objects:  16% (14/84)\u001b[K\rremote: Counting objects:  17% (15/84)\u001b[K\rremote: Counting objects:  19% (16/84)\u001b[K\rremote: Counting objects:  20% (17/84)\u001b[K\rremote: Counting objects:  21% (18/84)\u001b[K\rremote: Counting objects:  22% (19/84)\u001b[K\rremote: Counting objects:  23% (20/84)\u001b[K\rremote: Counting objects:  25% (21/84)\u001b[K\rremote: Counting objects:  26% (22/84)\u001b[K\rremote: Counting objects:  27% (23/84)\u001b[K\rremote: Counting objects:  28% (24/84)\u001b[K\rremote: Counting objects:  29% (25/84)\u001b[K\rremote: Counting objects:  30% (26/84)\u001b[K\rremote: Counting objects:  32% (27/84)\u001b[K\rremote: Counting objects:  33% (28/84)\u001b[K\rremote: Counting objects:  34% (29/84)\u001b[K\rremote: Counting objects:  35% (30/84)\u001b[K\rremote: Counting objects:  36% (31/84)\u001b[K\rremote: Counting objects:  38% (32/84)\u001b[K\rremote: Counting objects:  39% (33/84)\u001b[K\rremote: Counting objects:  40% (34/84)\u001b[K\rremote: Counting objects:  41% (35/84)\u001b[K\rremote: Counting objects:  42% (36/84)\u001b[K\rremote: Counting objects:  44% (37/84)\u001b[K\rremote: Counting objects:  45% (38/84)\u001b[K\rremote: Counting objects:  46% (39/84)\u001b[K\rremote: Counting objects:  47% (40/84)\u001b[K\rremote: Counting objects:  48% (41/84)\u001b[K\rremote: Counting objects:  50% (42/84)\u001b[K\rremote: Counting objects:  51% (43/84)\u001b[K\rremote: Counting objects:  52% (44/84)\u001b[K\rremote: Counting objects:  53% (45/84)\u001b[K\rremote: Counting objects:  54% (46/84)\u001b[K\rremote: Counting objects:  55% (47/84)\u001b[K\rremote: Counting objects:  57% (48/84)\u001b[K\rremote: Counting objects:  58% (49/84)\u001b[K\rremote: Counting objects:  59% (50/84)\u001b[K\rremote: Counting objects:  60% (51/84)\u001b[K\rremote: Counting objects:  61% (52/84)\u001b[K\rremote: Counting objects:  63% (53/84)\u001b[K\rremote: Counting objects:  64% (54/84)\u001b[K\rremote: Counting objects:  65% (55/84)\u001b[K\rremote: Counting objects:  66% (56/84)\u001b[K\rremote: Counting objects:  67% (57/84)\u001b[K\rremote: Counting objects:  69% (58/84)\u001b[K\rremote: Counting objects:  70% (59/84)\u001b[K\rremote: Counting objects:  71% (60/84)\u001b[K\rremote: Counting objects:  72% (61/84)\u001b[K\rremote: Counting objects:  73% (62/84)\u001b[K\rremote: Counting objects:  75% (63/84)\u001b[K\rremote: Counting objects:  76% (64/84)\u001b[K\rremote: Counting objects:  77% (65/84)\u001b[K\rremote: Counting objects:  78% (66/84)\u001b[K\rremote: Counting objects:  79% (67/84)\u001b[K\rremote: Counting objects:  80% (68/84)\u001b[K\rremote: Counting objects:  82% (69/84)\u001b[K\rremote: Counting objects:  83% (70/84)\u001b[K\rremote: Counting objects:  84% (71/84)\u001b[K\rremote: Counting objects:  85% (72/84)\u001b[K\rremote: Counting objects:  86% (73/84)\u001b[K\rremote: Counting objects:  88% (74/84)\u001b[K\rremote: Counting objects:  89% (75/84)\u001b[K\rremote: Counting objects:  90% (76/84)\u001b[K\rremote: Counting objects:  91% (77/84)\u001b[K\rremote: Counting objects:  92% (78/84)\u001b[K\rremote: Counting objects:  94% (79/84)\u001b[K\rremote: Counting objects:  95% (80/84)\u001b[K\rremote: Counting objects:  96% (81/84)\u001b[K\rremote: Counting objects:  97% (82/84)\u001b[K\rremote: Counting objects:  98% (83/84)\u001b[K\rremote: Counting objects: 100% (84/84)\u001b[K\rremote: Counting objects: 100% (84/84), done.\u001b[K\n",
            "remote: Compressing objects:   1% (1/69)\u001b[K\rremote: Compressing objects:   2% (2/69)\u001b[K\rremote: Compressing objects:   4% (3/69)\u001b[K\rremote: Compressing objects:   5% (4/69)\u001b[K\rremote: Compressing objects:   7% (5/69)\u001b[K\rremote: Compressing objects:   8% (6/69)\u001b[K\rremote: Compressing objects:  10% (7/69)\u001b[K\rremote: Compressing objects:  11% (8/69)\u001b[K\rremote: Compressing objects:  13% (9/69)\u001b[K\rremote: Compressing objects:  14% (10/69)\u001b[K\rremote: Compressing objects:  15% (11/69)\u001b[K\rremote: Compressing objects:  17% (12/69)\u001b[K\rremote: Compressing objects:  18% (13/69)\u001b[K\rremote: Compressing objects:  20% (14/69)\u001b[K\rremote: Compressing objects:  21% (15/69)\u001b[K\rremote: Compressing objects:  23% (16/69)\u001b[K\rremote: Compressing objects:  24% (17/69)\u001b[K\rremote: Compressing objects:  26% (18/69)\u001b[K\rremote: Compressing objects:  27% (19/69)\u001b[K\rremote: Compressing objects:  28% (20/69)\u001b[K\rremote: Compressing objects:  30% (21/69)\u001b[K\rremote: Compressing objects:  31% (22/69)\u001b[K\rremote: Compressing objects:  33% (23/69)\u001b[K\rremote: Compressing objects:  34% (24/69)\u001b[K\rremote: Compressing objects:  36% (25/69)\u001b[K\rremote: Compressing objects:  37% (26/69)\u001b[K\rremote: Compressing objects:  39% (27/69)\u001b[K\rremote: Compressing objects:  40% (28/69)\u001b[K\rremote: Compressing objects:  42% (29/69)\u001b[K\rremote: Compressing objects:  43% (30/69)\u001b[K\rremote: Compressing objects:  44% (31/69)\u001b[K\rremote: Compressing objects:  46% (32/69)\u001b[K\rremote: Compressing objects:  47% (33/69)\u001b[K\rremote: Compressing objects:  49% (34/69)\u001b[K\rremote: Compressing objects:  50% (35/69)\u001b[K\rremote: Compressing objects:  52% (36/69)\u001b[K\rremote: Compressing objects:  53% (37/69)\u001b[K\rremote: Compressing objects:  55% (38/69)\u001b[K\rremote: Compressing objects:  56% (39/69)\u001b[K\rremote: Compressing objects:  57% (40/69)\u001b[K\rremote: Compressing objects:  59% (41/69)\u001b[K\rremote: Compressing objects:  60% (42/69)\u001b[K\rremote: Compressing objects:  62% (43/69)\u001b[K\rremote: Compressing objects:  63% (44/69)\u001b[K\rremote: Compressing objects:  65% (45/69)\u001b[K\rremote: Compressing objects:  66% (46/69)\u001b[K\rremote: Compressing objects:  68% (47/69)\u001b[K\rremote: Compressing objects:  69% (48/69)\u001b[K\rremote: Compressing objects:  71% (49/69)\u001b[K\rremote: Compressing objects:  72% (50/69)\u001b[K\rremote: Compressing objects:  73% (51/69)\u001b[K\rremote: Compressing objects:  75% (52/69)\u001b[K\rremote: Compressing objects:  76% (53/69)\u001b[K\rremote: Compressing objects:  78% (54/69)\u001b[K\rremote: Compressing objects:  79% (55/69)\u001b[K\rremote: Compressing objects:  81% (56/69)\u001b[K\rremote: Compressing objects:  82% (57/69)\u001b[K\rremote: Compressing objects:  84% (58/69)\u001b[K\rremote: Compressing objects:  85% (59/69)\u001b[K\rremote: Compressing objects:  86% (60/69)\u001b[K\rremote: Compressing objects:  88% (61/69)\u001b[K\rremote: Compressing objects:  89% (62/69)\u001b[K\rremote: Compressing objects:  91% (63/69)\u001b[K\rremote: Compressing objects:  92% (64/69)\u001b[K\rremote: Compressing objects:  94% (65/69)\u001b[K\rremote: Compressing objects:  95% (66/69)\u001b[K\rremote: Compressing objects:  97% (67/69)\u001b[K\rremote: Compressing objects:  98% (68/69)\u001b[K\rremote: Compressing objects: 100% (69/69)\u001b[K\rremote: Compressing objects: 100% (69/69), done.\u001b[K\n",
            "Receiving objects:   0% (1/1682)   \rReceiving objects:   1% (17/1682)   \rReceiving objects:   2% (34/1682)   \rReceiving objects:   3% (51/1682)   \rReceiving objects:   4% (68/1682)   \rReceiving objects:   5% (85/1682)   \rReceiving objects:   6% (101/1682)   \rReceiving objects:   7% (118/1682)   \rReceiving objects:   8% (135/1682)   \rReceiving objects:   9% (152/1682)   \rReceiving objects:  10% (169/1682)   \rReceiving objects:  11% (186/1682)   \rReceiving objects:  12% (202/1682)   \rReceiving objects:  13% (219/1682)   \rReceiving objects:  14% (236/1682)   \rReceiving objects:  15% (253/1682)   \rReceiving objects:  16% (270/1682)   \rReceiving objects:  17% (286/1682)   \rReceiving objects:  18% (303/1682)   \rReceiving objects:  19% (320/1682)   \rReceiving objects:  20% (337/1682)   \rReceiving objects:  21% (354/1682)   \rReceiving objects:  22% (371/1682)   \rReceiving objects:  23% (387/1682)   \rReceiving objects:  24% (404/1682)   \rReceiving objects:  25% (421/1682)   \rReceiving objects:  26% (438/1682)   \rReceiving objects:  27% (455/1682)   \rReceiving objects:  28% (471/1682)   \rReceiving objects:  29% (488/1682)   \rReceiving objects:  30% (505/1682)   \rReceiving objects:  31% (522/1682)   \rReceiving objects:  32% (539/1682)   \rReceiving objects:  33% (556/1682)   \rReceiving objects:  34% (572/1682)   \rReceiving objects:  35% (589/1682)   \rReceiving objects:  36% (606/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  37% (623/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  38% (640/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  39% (656/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  40% (673/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  41% (690/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  42% (707/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  43% (724/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  44% (741/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  45% (757/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  46% (774/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  47% (791/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  48% (808/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  49% (825/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  50% (841/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  51% (858/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  52% (875/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  53% (892/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  54% (909/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  55% (926/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  56% (942/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  57% (959/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  58% (976/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  59% (993/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  60% (1010/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  61% (1027/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  62% (1043/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  63% (1060/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  64% (1077/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  65% (1094/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  66% (1111/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  67% (1127/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  68% (1144/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  69% (1161/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  70% (1178/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  71% (1195/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  72% (1212/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  73% (1228/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  74% (1245/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  75% (1262/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  76% (1279/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  77% (1296/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  78% (1312/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  79% (1329/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  80% (1346/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  81% (1363/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  82% (1380/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  83% (1397/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  84% (1413/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  85% (1430/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  86% (1447/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  87% (1464/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  88% (1481/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  89% (1497/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  90% (1514/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  91% (1531/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  92% (1548/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  93% (1565/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  94% (1582/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  95% (1598/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  96% (1615/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  97% (1632/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects:  98% (1649/1682), 16.97 MiB | 33.94 MiB/s   \rremote: Total 1682 (delta 34), reused 40 (delta 15), pack-reused 1598\u001b[K\n",
            "Receiving objects:  99% (1666/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects: 100% (1682/1682), 16.97 MiB | 33.94 MiB/s   \rReceiving objects: 100% (1682/1682), 25.35 MiB | 37.78 MiB/s, done.\n",
            "Resolving deltas:   0% (0/944)   \rResolving deltas:   1% (12/944)   \rResolving deltas:   2% (22/944)   \rResolving deltas:   3% (30/944)   \rResolving deltas:   4% (46/944)   \rResolving deltas:   6% (60/944)   \rResolving deltas:   7% (68/944)   \rResolving deltas:   8% (77/944)   \rResolving deltas:   9% (85/944)   \rResolving deltas:  11% (110/944)   \rResolving deltas:  12% (115/944)   \rResolving deltas:  13% (124/944)   \rResolving deltas:  14% (136/944)   \rResolving deltas:  15% (143/944)   \rResolving deltas:  16% (156/944)   \rResolving deltas:  17% (162/944)   \rResolving deltas:  18% (170/944)   \rResolving deltas:  19% (188/944)   \rResolving deltas:  20% (189/944)   \rResolving deltas:  21% (199/944)   \rResolving deltas:  22% (210/944)   \rResolving deltas:  23% (219/944)   \rResolving deltas:  24% (228/944)   \rResolving deltas:  26% (248/944)   \rResolving deltas:  27% (255/944)   \rResolving deltas:  30% (286/944)   \rResolving deltas:  31% (302/944)   \rResolving deltas:  32% (303/944)   \rResolving deltas:  33% (313/944)   \rResolving deltas:  34% (321/944)   \rResolving deltas:  35% (331/944)   \rResolving deltas:  36% (342/944)   \rResolving deltas:  37% (351/944)   \rResolving deltas:  38% (361/944)   \rResolving deltas:  42% (405/944)   \rResolving deltas:  44% (417/944)   \rResolving deltas:  45% (425/944)   \rResolving deltas:  46% (441/944)   \rResolving deltas:  47% (444/944)   \rResolving deltas:  48% (458/944)   \rResolving deltas:  49% (469/944)   \rResolving deltas:  50% (472/944)   \rResolving deltas:  52% (497/944)   \rResolving deltas:  57% (542/944)   \rResolving deltas:  58% (549/944)   \rResolving deltas:  59% (560/944)   \rResolving deltas:  60% (567/944)   \rResolving deltas:  61% (576/944)   \rResolving deltas:  63% (595/944)   \rResolving deltas:  64% (613/944)   \rResolving deltas:  70% (663/944)   \rResolving deltas:  71% (673/944)   \rResolving deltas:  72% (684/944)   \rResolving deltas:  73% (691/944)   \rResolving deltas:  74% (699/944)   \rResolving deltas:  75% (710/944)   \rResolving deltas:  76% (723/944)   \rResolving deltas:  78% (737/944)   \rResolving deltas:  80% (757/944)   \rResolving deltas:  81% (769/944)   \rResolving deltas:  83% (792/944)   \rResolving deltas:  84% (799/944)   \rResolving deltas:  86% (817/944)   \rResolving deltas:  87% (827/944)   \rResolving deltas:  88% (831/944)   \rResolving deltas:  90% (850/944)   \rResolving deltas:  91% (862/944)   \rResolving deltas:  92% (870/944)   \rResolving deltas:  94% (890/944)   \rResolving deltas:  95% (899/944)   \rResolving deltas:  96% (907/944)   \rResolving deltas:  97% (922/944)   \rResolving deltas:  98% (928/944)   \rResolving deltas:  99% (940/944)   \rResolving deltas: 100% (944/944)   \rResolving deltas: 100% (944/944), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFZyw9Tn3NHD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch \n",
        "import torchvision \n",
        "import torchvision.transforms as transforms \n",
        "import matplotlib.pyplot as plt \n",
        "import time \n",
        "import sys \n",
        "import sys \n",
        "sys.path.append('Dive-into-DL-PyTorch/code')\n",
        "import d2lzh_pytorch as d2l"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTOkc1Np7VGS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "4034f3db-0199-4056-ad08-e715fc7db220"
      },
      "source": [
        "mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor())\n",
        "mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "26427392it [00:02, 10562672.48it/s]                             \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/Datasets/FashionMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "32768it [00:00, 74696.71it/s]                            \n",
            "0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/Datasets/FashionMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "4423680it [00:01, 3007932.26it/s]                            \n",
            "0it [00:00, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/Datasets/FashionMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "8192it [00:00, 26941.90it/s]            "
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /root/Datasets/FashionMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/Datasets/FashionMNIST/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_McodO9-HoA",
        "colab_type": "code",
        "outputId": "09b394d2-be37-44bd-fd40-30dd4224cff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(type(mnist_train))\n",
        "print(len(mnist_train), len(mnist_test))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'torchvision.datasets.mnist.FashionMNIST'>\n",
            "60000 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkIttVoZ-slx",
        "colab_type": "code",
        "outputId": "46af5279-4b17-4bc8-8b60-beb59ae28e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "feature, label = mnist_train[0]\n",
        "print(feature.shape, label)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 28, 28]) 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tHWTrnW-5ue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_fashion_mnist_labels(labels):\n",
        "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', \n",
        "            'scandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
        "    return [text_labels[int(i)] for i in labels]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxzglQuK0knn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_fashion_mnist(images, labels):\n",
        "    d2l.use_svg_display()\n",
        "    _, figs = plt.subplots(1, len(images), figsize=(12, 12))\n",
        "    for f, img, lbl in zip(figs, images, labels):\n",
        "        f.imshow(img.view((28, 28)).numpy())\n",
        "        f.set_title(lbl)\n",
        "        f.axes.get_xaxis().set_visible(False)\n",
        "        f.axes.get_yaxis().set_visible(False)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcYjqnOb1aJC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "outputId": "9aca89ec-8753-4762-b92a-91c8ce3dbef4"
      },
      "source": [
        "X, y = [], []\n",
        "for i in range(9):\n",
        "    X.append(mnist_train[i][0])\n",
        "    y.append(mnist_train[i][1])\n",
        "show_fashion_mnist(X, get_fashion_mnist_labels(y))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTGqY9AP11mu",
        "colab_type": "text"
      },
      "source": [
        "### 3.5.2 读取小批量"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBrNRHNS2iJ-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 256 \n",
        "num_workers = 4 \n",
        "train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
        "test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ybfM_vP3Mum",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53e0ea6b-9fab-4d67-ba19-8c0b883d7cd6"
      },
      "source": [
        "start = time.time()\n",
        "for X, y in train_iter:\n",
        "    continue \n",
        "print('%.2f sec' % (time.time() - start))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.86 sec\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}